Prediction of Epileptic Seizures PhD Conversion Seminar

1. Prediction of Epileptic Seizures PhD Conversion Seminar Elma O’Sullivan-Greene Life Sciences, NICTA VRL Dept. Electrical & Electronic Engineering, The University of Melbourne elmao@ee.unimelb.edu.au Supervisors: Prof. Iven Mareels Dr. Levin Kuhlmann A/Prof. Anthony Burkitt Dr. Chung-Yao Kao

2. Talk Outline • Epilepsy: a disorder of the brain • Data available for engineering analysis • Current approaches to epileptic seizure prediction, and their limitations • Work completed and in progress • Proposed avenues for project

3. Talk Outline • Epilepsy: a disorder of the brain • Data available for engineering analysis • Current approaches to epileptic seizure prediction, and their limitations • Work completed and in progress • Proposed avenues for project

4. Epilepsy: a disorder of the brain • Epilepsy is a neurological disorder • Characterised by recurrent “seizures” • Associated with abnormally excessive or synchronous neuronal activity in the brain • Most common serious neurological condition • Prevalence of epilepsy varies across geographical regions within the range of 0.5% to 4% of the total population (WHO) • Current Treatment • AED (Antiepileptic Drugs) - undesirable side-effects • Surgical removal of the epileptic brain tissue

5. Motivation For Seizure Prediction • The ability to predict seizures would have a profound impact on the quality of life of epilepsy suffers. • Our proposed solution • An Implantable device incorporating • seizure prediction • short-term electric stimulation treatment for seizure prevention • Continuous electric stimulation is in use, and shows good results in many patients (unknown side effects for long term use) • No robust seizure prediction algorithm has been published to date

6. Talk Outline • Epilepsy: a disorder of the brain • Data available for engineering analysis • Current approaches to epileptic seizure prediction, and their limitations • Work completed and in progress • Proposed avenues for project

7. Data Source: Electroencephalography (EEG) • Recordings of the fluctuating electric fields of the brain • Electric fields due to ionic currents in the extra cellular fluid • Neurons (nerve cells) choose when to fire impulses based on this ionic current information

8. Data Source: Electroencephalography (EEG) • Recordings of the fluctuating electric fields of the brain • Scalp EEG data • Intracranial EEG data

9. Talk Outline • Epilepsy: a disorder of the brain • Data available for engineering analysis • Current approaches to epileptic seizure prediction, and their limitations • Work completed and in progress • Proposed avenues for project

10. Can Seizures Be Predicted? • Evidence for a definable pre-ictal (pre-seizure) period • Clinically undisputed indicative systematic changes are present in some patients prior to seizure onset • Mood changes, nausea, headache • Several signal processing studies argue that a pre-ictal state can be defined based on • Measures of synchronisation between EEG channels • Non-linear dynamics measures

11. Current Prediction Approaches • Linear Approaches • Spectral analysis • Linear Modelling • Energy measures • Minimal success: brain function nonlinear? • Nonlinear Approaches • Based on state space reconstruction • Dimension • Lyapunov Exponents • Entropy • Minimal success: initial promising results failed to be reproduced with other data sets

12. State Space Reconstruction/ Delay Embedding N at least O(1015)

13. State Space Reconstruction/ Delay Embedding Combine to reconstruct an N-dimensional system

14. Limitations of Delay Reconstruction • The original framework (Takens’/ Aeyels) for delay reconstruction requires: • Stationarity of the dataset • Noise free data set • A time series from an autonomous dynamical system • Low dimensionality of underlying dynamical system • However the EEG is ultimately an unsuitable signal for this framework • Highly non-stationary data set • High levels of measurement noise in EEG recordings (artefact) • The brain is not an autonomous system (brain processes external inputs) • No conclusive evidence that the brain/ epileptic events are low dimensional

15. Talk Outline • Epilepsy: a disorder of the brain • Data available for engineering analysis • Current approaches to epileptic seizure prediction, and their limitations • Work completed and in progress • Proposed avenues for project

16. Work Completed and in Progress • Modification of the EEG signal for delay reconstruction • Addressing the noise limitation • Taking the difference between 2 closely spaced intracranial electrodes Consider the Brain as: • spatially distributed system • interacting distinct local subsystems • Cancel common mode input from far away dynamical subsystems (Stark) • Representation of local dynamics • Significant reduction in common mode artefact (50 Hz mains pick-up)

17. Work Completed and in Progress • Modification of the EEG signal for delay reconstruction • Addressing the low dimensionality limitation • Hypothesis: The brain is lower-dimensional during a seizure • Perhaps there is enough stationary data in the period just prior to a seizure to warrant a reconstruction

18. An existing seizure prediction algorithm: • Dynamical Similarity Index (DSI) • Le Van Quyen (1999) • Creates templates of brain dynamics from delay reconstruction of EEG data • Seizure anticipation state declared for large sustained deviations of dynamic template from reference (far from seizure)

19. Work Completed and in Progress • Application of modified EEG signal to DSI algorithm • Reference template from pre-ictal data (low-dimensional/stationarity considerations) • EEG signal used: difference between 2 closely spaced intracranial electrodes (noise consideration) • Preliminary results • Sensitivity: 25%-100% across 3 patients • False Positive rate: 1-6.6 FP/hr across 3 patients

20. Work Completed and in Progress • No major improvement seen with preliminary results over original DSI algorithm • Why? • Pre-ictal low dimensionality of underlying system is an unproven hypothesis • Other noise: muscle artefact, cardiac artefact … • Conclusion • Future prediction methods should concentrate on non-delay-reconstruction based methods

21. Talk Outline • Epilepsy: a disorder of the brain • Data available for engineering analysis • Current approaches to epileptic seizure prediction, and their limitations • Work completed and in progress • Proposed avenues for project

22. Project Proposal • Nonlinear System analysis without reconstruction • Data-driven pathway Brain System Unknown state space system, F xk+1=F (xk , ωk) Measured EEG Data Represented by the function, H zk=H (xk , ωk) ? Epilepsy Prediction Represented by the function, G yk+1=G (xk , ωk)

23. Project Proposal - Entropy via Data Compression • An entropy measure as a prediction candidate • Low-dimensional object indicative of underlying brain state • Entropy, as measured in the brain, can be viewed as • a measure of how “chaotic” the brain system is • a measure of information transfer in the brain

24. Entropy via Data Compression Techniques Instead of computing entropy via delay reconstruction…. • Estimating entropy via Data-Compression Techniques • Markov Model • Context-Tree Model • Model based on Independent Component Analysis (ICA) • Let observed time-series data (EEG) be an element of a finite alphabet of symbols • Advantages of this approach • More robust in the presence of noise • Does not require stationarity of the data set • Can be applied to High Dimensional Systems

25. Entropy Estimation from a Markov Model • Markov model: • Estimates future symbols based on k-past past symbols • Symbolic time series analysis

26. Entropy Estimation from a Weighted Context Tree • Weighted Context tree: • Estimates future symbols based on k-past past symbols • Each node or “context” contains information of symbol history • Automated recursive weight probability associated with each context • Contexts automatically discarded on basis of improved performance • Entropy: h = L / N L=Source Code length N=Time

27. Entropy Estimation from an ICA based model • Measured EEG Channels: x = A s • Find the transformation of the data W = A-1such that the coding lengths of the components are minimised • Non-linear independent component methods • Using several EEG channels: spatial information Statistically Independent components x = f( s ) y = h( x )

28. Seizure Prediction Proposal • Have discussed Entropy as a seizure prediction candidate as estimated from data compression techniques. • Next: An alternative probabilistic approach to data-based seizure prediction……

29. Bifurcation Phenomenon – prediction by tracking the trajectory of bifurcation parameter, μ, over time Bifurcation part of thalmo-cortical brain model, Robinson (2003) Seizure Prediction: Decision Markov Process • Motivation for a statistical decision model: • Dynamical systems representations of the epileptic brain: • Probabilistic Transitions between two chaotic attractors Normal Epileptic Phase portrait of computer model of brain’s thalmo-cortical network, Lopes Da Silva (2003)

30. Seizure Prediction: Decision Markov Process • 3 state model • Transition probabilities tij assigned through analysis of EEG data • Potential for intervention applications: control input to minimise the transition to seizure state

31. Conclusion: Research Proposal • Proposed Avenues for Seizure Prediction: • Entropy as estimated from data compression techniques • Markov Process • Context Tree • Independent Component Analysis • Decision Markov Process • Potential for the theoretical expansion of dynamical system time-series analysis • for the application of real world biological data

32. Thank you for your attention Questions? elmao@ee.unimelb.edu.au